Overview

Brought to you by YData

Dataset statistics

Number of variables16
Number of observations18648
Missing cells0
Missing cells (%)0.0%
Duplicate rows1225
Duplicate rows (%)6.6%
Total size in memory2.4 MiB
Average record size in memory136.0 B

Variable types

Text3
Numeric5
Categorical7
Boolean1

Alerts

Dataset has 1225 (6.6%) duplicate rowsDuplicates
Doors is highly imbalanced (81.2%)Imbalance
Wheel is highly imbalanced (60.5%)Imbalance
Mileage is highly skewed (γ1 = 38.29564934)Skewed
Mileage has 703 (3.8%) zerosZeros
Airbags has 2355 (12.6%) zerosZeros

Reproduction

Analysis started2024-10-11 09:50:10.087833
Analysis finished2024-10-11 09:50:13.794885
Duration3.71 seconds
Software versionydata-profiling vv4.10.0
Download configurationconfig.json

Variables

Distinct63
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size291.4 KiB
2024-10-11T12:50:13.890402image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length13
Median length11
Mean length6.8181574
Min length3

Characters and Unicode

Total characters127145
Distinct characters31
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8 ?
Unique (%)< 0.1%

Sample

1st rowLEXUS
2nd rowCHEVROLET
3rd rowHONDA
4th rowFORD
5th rowHONDA
ValueCountFrequency (%)
hyundai 3700
19.8%
toyota 3583
19.2%
mercedes-benz 1979
10.6%
ford 1060
 
5.7%
chevrolet 1043
 
5.6%
bmw 997
 
5.3%
honda 956
 
5.1%
lexus 895
 
4.8%
nissan 645
 
3.5%
volkswagen 571
 
3.1%
Other values (55) 3259
17.4%
2024-10-11T12:50:14.060084image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E 12517
 
9.8%
O 12103
 
9.5%
A 11703
 
9.2%
N 9595
 
7.5%
T 8681
 
6.8%
D 8472
 
6.7%
Y 7754
 
6.1%
S 6658
 
5.2%
I 6300
 
5.0%
H 6073
 
4.8%
Other values (21) 37289
29.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 127145
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 12517
 
9.8%
O 12103
 
9.5%
A 11703
 
9.2%
N 9595
 
7.5%
T 8681
 
6.8%
D 8472
 
6.7%
Y 7754
 
6.1%
S 6658
 
5.2%
I 6300
 
5.0%
H 6073
 
4.8%
Other values (21) 37289
29.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 127145
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 12517
 
9.8%
O 12103
 
9.5%
A 11703
 
9.2%
N 9595
 
7.5%
T 8681
 
6.8%
D 8472
 
6.7%
Y 7754
 
6.1%
S 6658
 
5.2%
I 6300
 
5.0%
H 6073
 
4.8%
Other values (21) 37289
29.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 127145
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 12517
 
9.8%
O 12103
 
9.5%
A 11703
 
9.2%
N 9595
 
7.5%
T 8681
 
6.8%
D 8472
 
6.7%
Y 7754
 
6.1%
S 6658
 
5.2%
I 6300
 
5.0%
H 6073
 
4.8%
Other values (21) 37289
29.3%

Model
Text

Distinct1536
Distinct (%)8.2%
Missing0
Missing (%)0.0%
Memory size291.4 KiB
2024-10-11T12:50:14.269129image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length24
Median length23
Mean length5.8558559
Min length1

Characters and Unicode

Total characters109200
Distinct characters98
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique913 ?
Unique (%)4.9%

Sample

1st rowRX 450
2nd rowEquinox
3rd rowFIT
4th rowEscape
5th rowFIT
ValueCountFrequency (%)
prius 1243
 
5.2%
sonata 1093
 
4.6%
camry 1002
 
4.2%
elantra 950
 
4.0%
350 923
 
3.9%
e 834
 
3.5%
santa 505
 
2.1%
fe 505
 
2.1%
fit 469
 
2.0%
h1 435
 
1.8%
Other values (1050) 15816
66.5%
2024-10-11T12:50:14.552726image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 11309
 
10.4%
r 6993
 
6.4%
t 6241
 
5.7%
n 5686
 
5.2%
5127
 
4.7%
i 4670
 
4.3%
o 4465
 
4.1%
s 4122
 
3.8%
e 3914
 
3.6%
0 3815
 
3.5%
Other values (88) 52858
48.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 109200
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 11309
 
10.4%
r 6993
 
6.4%
t 6241
 
5.7%
n 5686
 
5.2%
5127
 
4.7%
i 4670
 
4.3%
o 4465
 
4.1%
s 4122
 
3.8%
e 3914
 
3.6%
0 3815
 
3.5%
Other values (88) 52858
48.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 109200
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 11309
 
10.4%
r 6993
 
6.4%
t 6241
 
5.7%
n 5686
 
5.2%
5127
 
4.7%
i 4670
 
4.3%
o 4465
 
4.1%
s 4122
 
3.8%
e 3914
 
3.6%
0 3815
 
3.5%
Other values (88) 52858
48.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 109200
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 11309
 
10.4%
r 6993
 
6.4%
t 6241
 
5.7%
n 5686
 
5.2%
5127
 
4.7%
i 4670
 
4.3%
o 4465
 
4.1%
s 4122
 
3.8%
e 3914
 
3.6%
0 3815
 
3.5%
Other values (88) 52858
48.4%

Prod. year
Real number (ℝ)

Distinct53
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2010.8446
Minimum1939
Maximum2020
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size291.4 KiB
2024-10-11T12:50:14.641772image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1939
5-th percentile1999
Q12009
median2012
Q32014
95-th percentile2017
Maximum2020
Range81
Interquartile range (IQR)5

Descriptive statistics

Standard deviation5.6426016
Coefficient of variation (CV)0.0028060853
Kurtosis10.654647
Mean2010.8446
Median Absolute Deviation (MAD)3
Skewness-2.0324607
Sum37498231
Variance31.838952
MonotonicityNot monotonic
2024-10-11T12:50:14.717702image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2012 2119
11.4%
2014 2059
11.0%
2013 1895
10.2%
2011 1577
 
8.5%
2015 1492
 
8.0%
2010 1460
 
7.8%
2016 1402
 
7.5%
2017 909
 
4.9%
2008 729
 
3.9%
2009 594
 
3.2%
Other values (43) 4412
23.7%
ValueCountFrequency (%)
1939 3
< 0.1%
1947 1
 
< 0.1%
1953 4
< 0.1%
1957 1
 
< 0.1%
1964 2
< 0.1%
1965 2
< 0.1%
1968 1
 
< 0.1%
1973 1
 
< 0.1%
1974 1
 
< 0.1%
1976 1
 
< 0.1%
ValueCountFrequency (%)
2020 28
 
0.2%
2019 273
 
1.5%
2018 455
 
2.4%
2017 909
4.9%
2016 1402
7.5%
2015 1492
8.0%
2014 2059
11.0%
2013 1895
10.2%
2012 2119
11.4%
2011 1577
8.5%

Category
Categorical

Distinct11
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size291.4 KiB
Sedan
8548 
Jeep
5193 
Hatchback
2797 
Minivan
 
628
Coupe
 
513
Other values (6)
969 

Length

Max length11
Median length9
Mean length5.5978121
Min length4

Characters and Unicode

Total characters104388
Distinct characters30
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowJeep
2nd rowJeep
3rd rowHatchback
4th rowJeep
5th rowHatchback

Common Values

ValueCountFrequency (%)
Sedan 8548
45.8%
Jeep 5193
27.8%
Hatchback 2797
 
15.0%
Minivan 628
 
3.4%
Coupe 513
 
2.8%
Universal 357
 
1.9%
Microbus 295
 
1.6%
Goods wagon 227
 
1.2%
Pickup 46
 
0.2%
Cabriolet 33
 
0.2%

Length

2024-10-11T12:50:14.799111image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
sedan 8548
45.3%
jeep 5193
27.5%
hatchback 2797
 
14.8%
minivan 628
 
3.3%
coupe 513
 
2.7%
universal 357
 
1.9%
microbus 295
 
1.6%
goods 227
 
1.2%
wagon 227
 
1.2%
pickup 46
 
0.2%
Other values (2) 44
 
0.2%

Most occurring characters

ValueCountFrequency (%)
e 19848
19.0%
a 15387
14.7%
n 10399
10.0%
d 8775
8.4%
S 8548
8.2%
c 5935
 
5.7%
p 5752
 
5.5%
J 5193
 
5.0%
b 3125
 
3.0%
k 2843
 
2.7%
Other values (20) 18583
17.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 104388
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 19848
19.0%
a 15387
14.7%
n 10399
10.0%
d 8775
8.4%
S 8548
8.2%
c 5935
 
5.7%
p 5752
 
5.5%
J 5193
 
5.0%
b 3125
 
3.0%
k 2843
 
2.7%
Other values (20) 18583
17.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 104388
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 19848
19.0%
a 15387
14.7%
n 10399
10.0%
d 8775
8.4%
S 8548
8.2%
c 5935
 
5.7%
p 5752
 
5.5%
J 5193
 
5.0%
b 3125
 
3.0%
k 2843
 
2.7%
Other values (20) 18583
17.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 104388
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 19848
19.0%
a 15387
14.7%
n 10399
10.0%
d 8775
8.4%
S 8548
8.2%
c 5935
 
5.7%
p 5752
 
5.5%
J 5193
 
5.0%
b 3125
 
3.0%
k 2843
 
2.7%
Other values (20) 18583
17.8%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size163.9 KiB
True
13471 
False
5177 
ValueCountFrequency (%)
True 13471
72.2%
False 5177
 
27.8%
2024-10-11T12:50:14.850533image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Fuel type
Categorical

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size291.4 KiB
Petrol
9763 
Diesel
3912 
Hybrid
3534 
LPG
 
885
CNG
 
469
Other values (2)
 
85

Length

Max length14
Median length6
Mean length5.8183183
Min length3

Characters and Unicode

Total characters108500
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowHybrid
2nd rowPetrol
3rd rowPetrol
4th rowHybrid
5th rowPetrol

Common Values

ValueCountFrequency (%)
Petrol 9763
52.4%
Diesel 3912
21.0%
Hybrid 3534
 
19.0%
LPG 885
 
4.7%
CNG 469
 
2.5%
Plug-in Hybrid 84
 
0.5%
Hydrogen 1
 
< 0.1%

Length

2024-10-11T12:50:14.928780image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-11T12:50:14.982749image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
petrol 9763
52.1%
diesel 3912
20.9%
hybrid 3618
 
19.3%
lpg 885
 
4.7%
cng 469
 
2.5%
plug-in 84
 
0.4%
hydrogen 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
e 17588
16.2%
l 13759
12.7%
r 13382
12.3%
P 10732
9.9%
o 9764
9.0%
t 9763
9.0%
i 7614
7.0%
D 3912
 
3.6%
s 3912
 
3.6%
d 3619
 
3.3%
Other values (12) 14455
13.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 108500
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 17588
16.2%
l 13759
12.7%
r 13382
12.3%
P 10732
9.9%
o 9764
9.0%
t 9763
9.0%
i 7614
7.0%
D 3912
 
3.6%
s 3912
 
3.6%
d 3619
 
3.3%
Other values (12) 14455
13.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 108500
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 17588
16.2%
l 13759
12.7%
r 13382
12.3%
P 10732
9.9%
o 9764
9.0%
t 9763
9.0%
i 7614
7.0%
D 3912
 
3.6%
s 3912
 
3.6%
d 3619
 
3.3%
Other values (12) 14455
13.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 108500
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 17588
16.2%
l 13759
12.7%
r 13382
12.3%
P 10732
9.9%
o 9764
9.0%
t 9763
9.0%
i 7614
7.0%
D 3912
 
3.6%
s 3912
 
3.6%
d 3619
 
3.3%
Other values (12) 14455
13.3%
Distinct103
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size291.4 KiB
2024-10-11T12:50:15.082688image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length9
Median length3
Mean length3.0397898
Min length1

Characters and Unicode

Total characters56686
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique12 ?
Unique (%)0.1%

Sample

1st row3.5
2nd row3
3rd row1.3
4th row2.5
5th row1.3
ValueCountFrequency (%)
2 3836
18.8%
2.5 2325
11.4%
1.8 1918
9.4%
turbo 1770
8.7%
1.6 1562
 
7.7%
1.5 1351
 
6.6%
3.5 1192
 
5.8%
2.4 1023
 
5.0%
3 806
 
3.9%
1.3 533
 
2.6%
Other values (59) 4102
20.1%
2024-10-11T12:50:15.250780image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 13708
24.2%
2 9151
16.1%
1 6398
11.3%
5 5137
 
9.1%
3 3684
 
6.5%
4 2409
 
4.2%
6 2094
 
3.7%
8 2058
 
3.6%
o 1770
 
3.1%
1770
 
3.1%
Other values (7) 8507
15.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 56686
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 13708
24.2%
2 9151
16.1%
1 6398
11.3%
5 5137
 
9.1%
3 3684
 
6.5%
4 2409
 
4.2%
6 2094
 
3.7%
8 2058
 
3.6%
o 1770
 
3.1%
1770
 
3.1%
Other values (7) 8507
15.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 56686
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 13708
24.2%
2 9151
16.1%
1 6398
11.3%
5 5137
 
9.1%
3 3684
 
6.5%
4 2409
 
4.2%
6 2094
 
3.7%
8 2058
 
3.6%
o 1770
 
3.1%
1770
 
3.1%
Other values (7) 8507
15.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 56686
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 13708
24.2%
2 9151
16.1%
1 6398
11.3%
5 5137
 
9.1%
3 3684
 
6.5%
4 2409
 
4.2%
6 2094
 
3.7%
8 2058
 
3.6%
o 1770
 
3.1%
1770
 
3.1%
Other values (7) 8507
15.0%

Mileage
Real number (ℝ)

SKEWED  ZEROS 

Distinct7594
Distinct (%)40.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1577387.2
Minimum0
Maximum2.1474836 × 109
Zeros703
Zeros (%)3.8%
Negative0
Negative (%)0.0%
Memory size291.4 KiB
2024-10-11T12:50:15.321707image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2261.9
Q171298.5
median127515
Q3190000
95-th percentile321187.8
Maximum2.1474836 × 109
Range2.1474836 × 109
Interquartile range (IQR)118701.5

Descriptive statistics

Standard deviation49161708
Coefficient of variation (CV)31.166545
Kurtosis1549.6717
Mean1577387.2
Median Absolute Deviation (MAD)58533
Skewness38.295649
Sum2.9415116 × 1010
Variance2.4168736 × 1015
MonotonicityNot monotonic
2024-10-11T12:50:15.385686image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 703
 
3.8%
200000 180
 
1.0%
150000 157
 
0.8%
160000 119
 
0.6%
180000 115
 
0.6%
100000 104
 
0.6%
1000 100
 
0.5%
170000 98
 
0.5%
120000 96
 
0.5%
130000 83
 
0.4%
Other values (7584) 16893
90.6%
ValueCountFrequency (%)
0 703
3.8%
13 1
 
< 0.1%
18 1
 
< 0.1%
98 1
 
< 0.1%
120 1
 
< 0.1%
261 1
 
< 0.1%
429 1
 
< 0.1%
498 1
 
< 0.1%
500 6
 
< 0.1%
554 1
 
< 0.1%
ValueCountFrequency (%)
2147483647 7
< 0.1%
1777777778 1
 
< 0.1%
1234567899 1
 
< 0.1%
1111111111 2
 
< 0.1%
999999999 5
< 0.1%
777777777 1
 
< 0.1%
222222222 1
 
< 0.1%
111111111 1
 
< 0.1%
58008888 1
 
< 0.1%
55556665 1
 
< 0.1%

Cylinders
Real number (ℝ)

Distinct13
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.5612398
Minimum1
Maximum16
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size291.4 KiB
2024-10-11T12:50:15.430923image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q14
median4
Q34
95-th percentile8
Maximum16
Range15
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.1777705
Coefficient of variation (CV)0.25821281
Kurtosis6.8389633
Mean4.5612398
Median Absolute Deviation (MAD)0
Skewness2.1328936
Sum85058
Variance1.3871435
MonotonicityNot monotonic
2024-10-11T12:50:15.480647image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
4 14055
75.4%
6 3279
 
17.6%
8 906
 
4.9%
5 168
 
0.9%
3 107
 
0.6%
2 42
 
0.2%
1 36
 
0.2%
12 32
 
0.2%
10 12
 
0.1%
16 5
 
< 0.1%
Other values (3) 6
 
< 0.1%
ValueCountFrequency (%)
1 36
 
0.2%
2 42
 
0.2%
3 107
 
0.6%
4 14055
75.4%
5 168
 
0.9%
6 3279
 
17.6%
7 4
 
< 0.1%
8 906
 
4.9%
9 1
 
< 0.1%
10 12
 
0.1%
ValueCountFrequency (%)
16 5
 
< 0.1%
14 1
 
< 0.1%
12 32
 
0.2%
10 12
 
0.1%
9 1
 
< 0.1%
8 906
 
4.9%
7 4
 
< 0.1%
6 3279
 
17.6%
5 168
 
0.9%
4 14055
75.4%

Gear box type
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size291.4 KiB
Automatic
13152 
Tiptronic
2932 
Manual
1833 
Variator
 
731

Length

Max length9
Median length9
Mean length8.6659159
Min length6

Characters and Unicode

Total characters161602
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAutomatic
2nd rowTiptronic
3rd rowVariator
4th rowAutomatic
5th rowAutomatic

Common Values

ValueCountFrequency (%)
Automatic 13152
70.5%
Tiptronic 2932
 
15.7%
Manual 1833
 
9.8%
Variator 731
 
3.9%

Length

2024-10-11T12:50:15.539105image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-11T12:50:15.583812image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
automatic 13152
70.5%
tiptronic 2932
 
15.7%
manual 1833
 
9.8%
variator 731
 
3.9%

Most occurring characters

ValueCountFrequency (%)
t 29967
18.5%
i 19747
12.2%
a 18280
11.3%
o 16815
10.4%
c 16084
10.0%
u 14985
9.3%
A 13152
8.1%
m 13152
8.1%
n 4765
 
2.9%
r 4394
 
2.7%
Other values (5) 10261
 
6.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 161602
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 29967
18.5%
i 19747
12.2%
a 18280
11.3%
o 16815
10.4%
c 16084
10.0%
u 14985
9.3%
A 13152
8.1%
m 13152
8.1%
n 4765
 
2.9%
r 4394
 
2.7%
Other values (5) 10261
 
6.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 161602
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 29967
18.5%
i 19747
12.2%
a 18280
11.3%
o 16815
10.4%
c 16084
10.0%
u 14985
9.3%
A 13152
8.1%
m 13152
8.1%
n 4765
 
2.9%
r 4394
 
2.7%
Other values (5) 10261
 
6.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 161602
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 29967
18.5%
i 19747
12.2%
a 18280
11.3%
o 16815
10.4%
c 16084
10.0%
u 14985
9.3%
A 13152
8.1%
m 13152
8.1%
n 4765
 
2.9%
r 4394
 
2.7%
Other values (5) 10261
 
6.3%

Drive wheels
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size291.4 KiB
Front
12620 
4x4
3812 
Rear
2216 

Length

Max length5
Median length5
Mean length4.4723295
Min length3

Characters and Unicode

Total characters83400
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4x4
2nd row4x4
3rd rowFront
4th row4x4
5th rowFront

Common Values

ValueCountFrequency (%)
Front 12620
67.7%
4x4 3812
 
20.4%
Rear 2216
 
11.9%

Length

2024-10-11T12:50:15.639300image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-11T12:50:15.880856image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
front 12620
67.7%
4x4 3812
 
20.4%
rear 2216
 
11.9%

Most occurring characters

ValueCountFrequency (%)
r 14836
17.8%
F 12620
15.1%
o 12620
15.1%
n 12620
15.1%
t 12620
15.1%
4 7624
9.1%
x 3812
 
4.6%
R 2216
 
2.7%
e 2216
 
2.7%
a 2216
 
2.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 83400
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 14836
17.8%
F 12620
15.1%
o 12620
15.1%
n 12620
15.1%
t 12620
15.1%
4 7624
9.1%
x 3812
 
4.6%
R 2216
 
2.7%
e 2216
 
2.7%
a 2216
 
2.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 83400
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 14836
17.8%
F 12620
15.1%
o 12620
15.1%
n 12620
15.1%
t 12620
15.1%
4 7624
9.1%
x 3812
 
4.6%
R 2216
 
2.7%
e 2216
 
2.7%
a 2216
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 83400
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 14836
17.8%
F 12620
15.1%
o 12620
15.1%
n 12620
15.1%
t 12620
15.1%
4 7624
9.1%
x 3812
 
4.6%
R 2216
 
2.7%
e 2216
 
2.7%
a 2216
 
2.7%

Doors
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size291.4 KiB
04-May
17781 
02-Mar
 
751
>5
 
116

Length

Max length6
Median length6
Mean length5.975118
Min length2

Characters and Unicode

Total characters111424
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row04-May
2nd row04-May
3rd row04-May
4th row04-May
5th row04-May

Common Values

ValueCountFrequency (%)
04-May 17781
95.4%
02-Mar 751
 
4.0%
>5 116
 
0.6%

Length

2024-10-11T12:50:15.934549image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-11T12:50:15.979440image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
04-may 17781
95.4%
02-mar 751
 
4.0%
5 116
 
0.6%

Most occurring characters

ValueCountFrequency (%)
0 18532
16.6%
- 18532
16.6%
M 18532
16.6%
a 18532
16.6%
4 17781
16.0%
y 17781
16.0%
2 751
 
0.7%
r 751
 
0.7%
> 116
 
0.1%
5 116
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 111424
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 18532
16.6%
- 18532
16.6%
M 18532
16.6%
a 18532
16.6%
4 17781
16.0%
y 17781
16.0%
2 751
 
0.7%
r 751
 
0.7%
> 116
 
0.1%
5 116
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 111424
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 18532
16.6%
- 18532
16.6%
M 18532
16.6%
a 18532
16.6%
4 17781
16.0%
y 17781
16.0%
2 751
 
0.7%
r 751
 
0.7%
> 116
 
0.1%
5 116
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 111424
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 18532
16.6%
- 18532
16.6%
M 18532
16.6%
a 18532
16.6%
4 17781
16.0%
y 17781
16.0%
2 751
 
0.7%
r 751
 
0.7%
> 116
 
0.1%
5 116
 
0.1%

Wheel
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size291.4 KiB
Left wheel
17196 
Right-hand drive
 
1452

Length

Max length16
Median length10
Mean length10.467181
Min length10

Characters and Unicode

Total characters195192
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLeft wheel
2nd rowLeft wheel
3rd rowRight-hand drive
4th rowLeft wheel
5th rowLeft wheel

Common Values

ValueCountFrequency (%)
Left wheel 17196
92.2%
Right-hand drive 1452
 
7.8%

Length

2024-10-11T12:50:16.029360image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-11T12:50:16.072238image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
left 17196
46.1%
wheel 17196
46.1%
right-hand 1452
 
3.9%
drive 1452
 
3.9%

Most occurring characters

ValueCountFrequency (%)
e 53040
27.2%
h 20100
 
10.3%
t 18648
 
9.6%
18648
 
9.6%
L 17196
 
8.8%
f 17196
 
8.8%
w 17196
 
8.8%
l 17196
 
8.8%
i 2904
 
1.5%
d 2904
 
1.5%
Other values (7) 10164
 
5.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 195192
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 53040
27.2%
h 20100
 
10.3%
t 18648
 
9.6%
18648
 
9.6%
L 17196
 
8.8%
f 17196
 
8.8%
w 17196
 
8.8%
l 17196
 
8.8%
i 2904
 
1.5%
d 2904
 
1.5%
Other values (7) 10164
 
5.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 195192
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 53040
27.2%
h 20100
 
10.3%
t 18648
 
9.6%
18648
 
9.6%
L 17196
 
8.8%
f 17196
 
8.8%
w 17196
 
8.8%
l 17196
 
8.8%
i 2904
 
1.5%
d 2904
 
1.5%
Other values (7) 10164
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 195192
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 53040
27.2%
h 20100
 
10.3%
t 18648
 
9.6%
18648
 
9.6%
L 17196
 
8.8%
f 17196
 
8.8%
w 17196
 
8.8%
l 17196
 
8.8%
i 2904
 
1.5%
d 2904
 
1.5%
Other values (7) 10164
 
5.2%

Color
Categorical

Distinct16
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size291.4 KiB
Black
4843 
White
4310 
Silver
3716 
Grey
2302 
Blue
1365 
Other values (11)
2112 

Length

Max length13
Median length5
Mean length5.0586122
Min length3

Characters and Unicode

Total characters94333
Distinct characters29
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSilver
2nd rowBlack
3rd rowBlack
4th rowWhite
5th rowSilver

Common Values

ValueCountFrequency (%)
Black 4843
26.0%
White 4310
23.1%
Silver 3716
19.9%
Grey 2302
12.3%
Blue 1365
 
7.3%
Red 620
 
3.3%
Green 320
 
1.7%
Orange 250
 
1.3%
Brown 181
 
1.0%
Carnelian red 176
 
0.9%
Other values (6) 565
 
3.0%

Length

2024-10-11T12:50:16.119071image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
black 4843
25.6%
white 4310
22.7%
silver 3716
19.6%
grey 2302
12.2%
blue 1487
 
7.8%
red 796
 
4.2%
green 320
 
1.7%
orange 250
 
1.3%
brown 181
 
1.0%
carnelian 176
 
0.9%
Other values (6) 565
 
3.0%

Most occurring characters

ValueCountFrequency (%)
e 14228
15.1%
l 10610
11.2%
i 8360
 
8.9%
r 7160
 
7.6%
B 6522
 
6.9%
a 5445
 
5.8%
k 4990
 
5.3%
c 4843
 
5.1%
W 4310
 
4.6%
h 4310
 
4.6%
Other values (19) 23555
25.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 94333
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 14228
15.1%
l 10610
11.2%
i 8360
 
8.9%
r 7160
 
7.6%
B 6522
 
6.9%
a 5445
 
5.8%
k 4990
 
5.3%
c 4843
 
5.1%
W 4310
 
4.6%
h 4310
 
4.6%
Other values (19) 23555
25.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 94333
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 14228
15.1%
l 10610
11.2%
i 8360
 
8.9%
r 7160
 
7.6%
B 6522
 
6.9%
a 5445
 
5.8%
k 4990
 
5.3%
c 4843
 
5.1%
W 4310
 
4.6%
h 4310
 
4.6%
Other values (19) 23555
25.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 94333
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 14228
15.1%
l 10610
11.2%
i 8360
 
8.9%
r 7160
 
7.6%
B 6522
 
6.9%
a 5445
 
5.8%
k 4990
 
5.3%
c 4843
 
5.1%
W 4310
 
4.6%
h 4310
 
4.6%
Other values (19) 23555
25.0%

Airbags
Real number (ℝ)

ZEROS 

Distinct17
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.5448305
Minimum0
Maximum16
Zeros2355
Zeros (%)12.6%
Negative0
Negative (%)0.0%
Memory size291.4 KiB
2024-10-11T12:50:16.162714image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14
median6
Q312
95-th percentile12
Maximum16
Range16
Interquartile range (IQR)8

Descriptive statistics

Standard deviation4.3145391
Coefficient of variation (CV)0.65922854
Kurtosis-1.3298157
Mean6.5448305
Median Absolute Deviation (MAD)4
Skewness0.092351365
Sum122048
Variance18.615248
MonotonicityNot monotonic
2024-10-11T12:50:16.215411image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
4 5663
30.4%
12 5411
29.0%
0 2355
12.6%
8 1559
 
8.4%
6 1276
 
6.8%
2 1049
 
5.6%
10 828
 
4.4%
5 103
 
0.6%
16 85
 
0.5%
7 84
 
0.5%
Other values (7) 235
 
1.3%
ValueCountFrequency (%)
0 2355
12.6%
1 76
 
0.4%
2 1049
 
5.6%
3 37
 
0.2%
4 5663
30.4%
5 103
 
0.6%
6 1276
 
6.8%
7 84
 
0.5%
8 1559
 
8.4%
9 61
 
0.3%
ValueCountFrequency (%)
16 85
 
0.5%
15 6
 
< 0.1%
14 20
 
0.1%
13 2
 
< 0.1%
12 5411
29.0%
11 33
 
0.2%
10 828
 
4.4%
9 61
 
0.3%
8 1559
 
8.4%
7 84
 
0.5%

price_levy_combined
Real number (ℝ)

Distinct7452
Distinct (%)40.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16450.774
Minimum1
Maximum74809
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size291.4 KiB
2024-10-11T12:50:16.271768image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1111
Q15663
median13485
Q322125
95-th percentile46217.15
Maximum74809
Range74808
Interquartile range (IQR)16462

Descriptive statistics

Standard deviation14138.254
Coefficient of variation (CV)0.85942787
Kurtosis1.7121123
Mean16450.774
Median Absolute Deviation (MAD)8154
Skewness1.324667
Sum3.0677403 × 108
Variance1.9989022 × 108
MonotonicityNot monotonic
2024-10-11T12:50:16.337528image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7840 173
 
0.9%
10976 141
 
0.8%
9408 126
 
0.7%
14113 122
 
0.7%
17249 112
 
0.6%
8467 93
 
0.5%
7213 92
 
0.5%
6272 91
 
0.5%
20385 91
 
0.5%
18817 90
 
0.5%
Other values (7442) 17517
93.9%
ValueCountFrequency (%)
1 1
 
< 0.1%
3 8
 
< 0.1%
6 3
 
< 0.1%
9 1
 
< 0.1%
19 1
 
< 0.1%
20 7
 
< 0.1%
25 16
 
0.1%
28 1
 
< 0.1%
30 76
0.4%
34 1
 
< 0.1%
ValueCountFrequency (%)
74809 1
 
< 0.1%
74802 1
 
< 0.1%
74791 1
 
< 0.1%
74733 1
 
< 0.1%
74717 7
< 0.1%
74699 1
 
< 0.1%
74589 1
 
< 0.1%
74494 1
 
< 0.1%
74407 1
 
< 0.1%
74389 1
 
< 0.1%

Interactions

2024-10-11T12:50:13.274615image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-11T12:50:10.728213image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-11T12:50:11.671510image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-11T12:50:12.710863image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-11T12:50:13.006006image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-11T12:50:13.330059image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-11T12:50:11.097522image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-11T12:50:11.825660image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-11T12:50:12.771229image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-11T12:50:13.062286image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-11T12:50:13.378416image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-11T12:50:11.141445image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-11T12:50:12.243361image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-11T12:50:12.825195image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-11T12:50:13.114737image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-11T12:50:13.435946image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-11T12:50:11.539589image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-11T12:50:12.602331image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-11T12:50:12.890577image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-11T12:50:13.170416image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-11T12:50:13.494274image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-11T12:50:11.593076image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-11T12:50:12.656514image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-11T12:50:12.950156image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-11T12:50:13.222219image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2024-10-11T12:50:16.387176image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
AirbagsCategoryColorCylindersDoorsDrive wheelsFuel typeGear box typeLeather interiorMileageProd. yearWheelprice_levy_combined
Airbags1.0000.1490.1020.2060.1240.2820.1950.3320.467-0.0280.1740.316-0.056
Category0.1491.0000.0940.1230.4410.4380.2340.3340.3780.0080.1950.3280.125
Color0.1020.0941.0000.0670.0770.1240.1830.1280.2070.0320.1100.1260.080
Cylinders0.2060.1230.0671.0000.0590.4580.0870.1360.2060.170-0.1790.089-0.038
Doors0.1240.4410.0770.0591.0000.1590.0770.2100.1230.0000.1610.0140.036
Drive wheels0.2820.4380.1240.4580.1591.0000.1820.2420.0890.0080.2400.0230.110
Fuel type0.1950.2340.1830.0870.0770.1821.0000.2280.1850.0410.1620.1480.162
Gear box type0.3320.3340.1280.1360.2100.2420.2281.0000.4630.0280.3220.2110.180
Leather interior0.4670.3780.2070.2060.1230.0890.1850.4631.0000.0370.3650.3460.269
Mileage-0.0280.0080.0320.1700.0000.0080.0410.0280.0371.000-0.3440.023-0.191
Prod. year0.1740.1950.110-0.1790.1610.2400.1620.3220.365-0.3441.0000.2360.287
Wheel0.3160.3280.1260.0890.0140.0230.1480.2110.3460.0230.2361.0000.247
price_levy_combined-0.0560.1250.080-0.0380.0360.1100.1620.1800.269-0.1910.2870.2471.000

Missing values

2024-10-11T12:50:13.586800image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-10-11T12:50:13.713549image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

ManufacturerModelProd. yearCategoryLeather interiorFuel typeEngine volumeMileageCylindersGear box typeDrive wheelsDoorsWheelColorAirbagsprice_levy_combined
0LEXUSRX 4502010JeepYesHybrid3.51860056.0Automatic4x404-MayLeft wheelSilver1214727
1CHEVROLETEquinox2011JeepNoPetrol31920006.0Tiptronic4x404-MayLeft wheelBlack817639
2HONDAFIT2006HatchbackNoPetrol1.32000004.0VariatorFront04-MayRight-hand driveBlack28467
3FORDEscape2011JeepYesHybrid2.51689664.0Automatic4x404-MayLeft wheelWhite04469
4HONDAFIT2014HatchbackYesPetrol1.3919014.0AutomaticFront04-MayLeft wheelSilver412172
5HYUNDAISanta FE2016JeepYesDiesel21609314.0AutomaticFront04-MayLeft wheelWhite440384
6TOYOTAPrius2010HatchbackYesHybrid1.82589094.0AutomaticFront04-MayLeft wheelWhite122564
7HYUNDAISonata2013SedanYesPetrol2.42161184.0AutomaticFront04-MayLeft wheelGrey121300
8TOYOTACamry2014SedanYesHybrid2.53980694.0AutomaticFront04-MayLeft wheelBlack121492
9LEXUSRX 3502007JeepYesPetrol3.51285006.0Automatic4x404-MayLeft wheelSilver1226657
ManufacturerModelProd. yearCategoryLeather interiorFuel typeEngine volumeMileageCylindersGear box typeDrive wheelsDoorsWheelColorAirbagsprice_levy_combined
18914MERCEDES-BENZE 3502014SedanYesDiesel3.52190306.0Automatic4x404-MayLeft wheelBlack1230846
18915MERCEDES-BENZE 3502008SedanYesDiesel3.51228746.0AutomaticRear04-MayLeft wheelBlack122556
18916TOYOTAPrius2008HatchbackNoHybrid1.51500004.0AutomaticFront04-MayLeft wheelSilver650
18917TOYOTAPrius2011HatchbackYesHybrid1.83073254.0AutomaticFront04-MayLeft wheelSilver121115
18918MERCEDES-BENZE 3502013SedanYesDiesel3.51078006.0AutomaticRear04-MayLeft wheelGrey126857
18919MERCEDES-BENZCLK 2001999CoupeYesCNG2.0 Turbo3000004.0ManualRear02-MarLeft wheelSilver58467
18920HYUNDAISonata2011SedanYesPetrol2.41616004.0TiptronicFront04-MayLeft wheelRed816512
18921HYUNDAITucson2010JeepYesDiesel21163654.0AutomaticFront04-MayLeft wheelGrey426944
18922CHEVROLETCaptiva2007JeepYesDiesel2512584.0AutomaticFront04-MayLeft wheelBlack46619
18923HYUNDAISonata2012SedanYesHybrid2.41869234.0AutomaticFront04-MayLeft wheelWhite121223

Duplicate rows

Most frequently occurring

ManufacturerModelProd. yearCategoryLeather interiorFuel typeEngine volumeMileageCylindersGear box typeDrive wheelsDoorsWheelColorAirbagsprice_levy_combined# duplicates
1101TOYOTACamry2018SedanYesHybrid2.5350584.0AutomaticFront04-MayLeft wheelWhite121942624
1067TOYOTACamry2012SedanYesHybrid2.53143734.0AutomaticFront04-MayLeft wheelGrey12117323
1092TOYOTACamry2014SedanYesHybrid2.51793814.0AutomaticFront04-MayLeft wheelBlack121152723
1064TOYOTACamry2012SedanYesHybrid2.51565184.0AutomaticFront04-MayLeft wheelWhite12438822
1076TOYOTACamry2013SedanYesHybrid2.51905494.0AutomaticFront04-MayLeft wheelBlack12579722
1087TOYOTACamry2014SedanYesHybrid2.51023974.0AutomaticFront04-MayLeft wheelBlack12117822
1093TOYOTACamry2014SedanYesHybrid2.53980694.0AutomaticFront04-MayLeft wheelBlack12149221
1065TOYOTACamry2012SedanYesHybrid2.51593794.0AutomaticFront04-MayLeft wheelWhite12391720
1091TOYOTACamry2014SedanYesHybrid2.51304784.0AutomaticFront04-MayLeft wheelWhite12125620
854MERCEDES-BENZE 3002017SedanYesPetrol2268024.0AutomaticRear04-MayLeft wheelBlack12133119